Frequently asked questions

Most projects follow a similar arc: discovery of your current environment and decision flows, design of new architecture, implementation of pipelines and models, and training your team to maintain it.

We typically start with a 2-3 week discovery phase before committing to timeline and scope. That makes estimates realistic and prevents scope creep.

Data Foundations projects typically run 10-16 weeks. Analytics projects are often 8-12 weeks. AI automation is usually 12-20 weeks depending on complexity and volume of exceptions.

These are working timelines—time spent actually building, not calendar time. Your team is engaged throughout, not handed a final deliverable at the end.

Incremental is almost always smarter. Build data foundations first—they unblock analytics. Get analytics working before automating. Each layer depends on the one below.

We design engagements with phases in mind. You can validate outcomes from phase 1 before committing to phase 2.

Yes. We work with most common modern stacks: Snowflake, BigQuery, Redshift, Databricks, Fivetran, dbt, Looker, Tableau, Datadog, and many others.

We don't believe in rip-and-replace. We integrate with what you have and upgrade strategically when it makes sense.

We've built connectors for systems with limited API support. Sometimes that means direct database access, sometimes CSV exports on a schedule, sometimes custom web scraping.

It's never elegant, but it works. We'll document the workaround clearly so your team understands the dependency.

Not necessarily. We often work within existing tool portfolios, connecting systems that weren't designed to talk to each other.

Sometimes a tool replacement makes sense—when it's the root cause of problems, when the cost of workarounds exceeds migration cost. We'll be honest if that's the case.

Only if we do it wrong. We design systems to be maintainable by your team—with clear logic, documented decisions, and no black boxes.

That takes more upfront time than a quick hack, but it's cheaper long-term and actually reduces debt by replacing fragile spreadsheet and manual processes.

We build human-in-the-loop, not fully automated. Humans review high-stakes decisions. We log everything so you can audit outcomes and understand when and why the AI gets things wrong.

We measure AI accuracy against reality—not against what the model thinks. If accuracy drops, we flag it.

We build fallback paths—ways to handle failures manually. Monitoring alerts your team. We document how to roll back or fix issues.

Automations fail. Ours fail safely, with audit trails, so your team can learn and improve.

We follow your organization's data governance policies and regulatory requirements (GDPR, HIPAA, SOC 2, etc.). Sensitive data is masked in development. Access is logged. Encryption is default.

Your security team should review our approach during discovery. No surprises.

Your team. We don't own anything. You own the warehouse, the pipelines, the dashboards, everything. We leave with complete documentation and knowledge transfer.

You can replace us tomorrow if you want. That's the point.

No. We offer support after launch because it's valuable and because your team will have questions as they learn the system. But you're not locked in.

If another team or vendor becomes a better fit later, you can migrate. We design with that possibility in mind.

Your team will have the skills, documentation, and codebase to maintain it. Systems aren't dependent on us being around.

We design for independence by default, train extensively, and document everything. That's not a failsafe, but it's the right foundation.

Architecture guides, data model documentation, runbooks for common operations, troubleshooting guides, decision logs explaining why we chose what we chose.

Not marketing documents. Practical, technical, written for your team to actually use.

As much as needed. Hands-on sessions walking through the system, how-to guides, Q&A sessions, code walkthroughs. Every team learns differently.

Training continues through launch so your team is confident before we step back.

We offer ongoing support if you want it—monthly check-ins, performance monitoring, optimization recommendations, help with new features.

That's optional. Some teams want to fly solo, and that's fine. Others prefer ongoing partnership. Whatever makes sense for you.

Varies widely based on scope, complexity, and team size. A straightforward 10-week analytics project might be $80-150K. A 16-week data foundation from scratch could be $150-300K.

We scope honestly after discovery conversations and don't surprise you. We're transparent about hours, rates, and assumptions.

We do both. Fixed-scope engagements work well when requirements are clear. Time-and-materials works for exploratory projects where scope will evolve.

We recommend fixed-scope with regular check-ins—you know the investment upfront, we manage scope tightly, and there's room to adjust if reality surprises us.

Very. We're not a black-box vendor. Your team provides context, validates designs, tests solutions. 1-2 people from your team in weekly working sessions is typical.

More involvement = better outcomes. Your team becomes the expert, not us.

We prefer working with your team. We'll handle specialized work—warehouse architecture, optimization—but your engineers should be involved in the implementation.

If you need to hire, we can help with requirements and onboarding. But building with your people is better than handing you a system only we understand.